Health Data Science MSc

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Organisation
Lancaster University
Start - End
3 Oct
Study Options
Full Time
Fee
GBP 11990 - 11990
Fee International
GBP 25700 - 25700

£10,000 Scholarships available to eligible applicants.

About

Deep statistical thinking combined with expertise in health and computer science is becoming increasingly fundamental in tackling public health problems across the world. The MSc in Health Data Science will equip you with advanced technical skills which will allow you to develop a career as a data-scientist in the health and care sector.

The MSc in Health Data Science, consists of an initial set of four core modules: “Statistical methods and models for health research”, “Programming for Health Data Science”, “Fundamentals for Health Data Science” and “Introduction to applied epidemiology”. These will allow you to develop and consolidate foundational skills in the three main areas of Health Data Science: epidemiology, statistics and computer science.

On completion of these modules, you will progress onto one of two pathways: “Global health” or “Health informatics”.

The “Global Health” pathway further develops your skills in statistical science with a focus on spatio-temporal methods of analysis of infectious diseases and how these are used to inform health policy decisions. The modules of the “Health Informatics'' pathway will focus on developing your knowledge and statistical skills in the analysis of routinely collected health data and in the economic evaluation of health care.

These two pathways will open you to new career opportunities as a data-scientist of a multidisciplinary team within international and national public health organizations and health care providers.

In addition to these taught modules, you will also undertake a 12-week project as part of a placement with one of our partners, which include the World Health Organization and the National Health Service. This will provide you with a fantastic opportunity to apply your skills and knowledge to real-world situations and challenges, allowing you to gain valuable professional experience as a part of a multidisciplinary team.

The placement project represents a substantial, independent research project. Supervised by an academic specialist, you will develop your ability to formulate a project plan, gather and analyse data, interpret your results, and present findings in a professional environment. This research will be an opportunity to bring together everything you have learnt over the year, expand your problem-solving abilities and manage a significant project. This will be a great experience for you to draw upon in your career.

Course structure

In the MSc in Health Data Science, you will study a set of 4 compulsory core modules, which will equip you with foundational skills in statistics, epidemiology and programming. You will then choose one of two possible learning pathways: “Global Health” or “Health Informatics”. Each pathway consists of 4 compulsory modules.

Core modules

Statistical methods and models for health research

This module introduces basic methods and models for statistical analysis with a strong focus on epidemiological applications. The module starts with the use of graphical tools for exploratory analysis: scatter plots; box plots; transformation of the response and outcome variables. The linear regression modelling framework is introduced with a focus on: critical evaluation of assumptions; link between regression analysis and ANOVA; interpretation of regression coefficients; multicollinearity and dealing with confounding factors; analysis of residuals. Building on this, the module covers Binomial and Poisson regression, and students will learn how to carry out hypothesis-testing through the analysis of deviance and the use of regression diagnostics. In the final part, the module gives an introduction to linear mixed models (random intercept and slope) and how to deal with over dispersion of count data.

Programming for health data science

The module will teach the fundamentals of computing: historical development of computational machines, data storage, computational processing, the development of computer science. This will be done using the R language for programming with focus on: simple calculations; structured programming with loops and conditions; data structures; writing functions. The students will be taught how to handle the R language for data file input/output; selection and filtering; exploratory graphics. Throughout the module, the students will also learn how to develop good computational research practice: project management for data and code; dynamic documents for reproducible research; source code management. In the second part of the module the students will learn the Python language basics: fundamentals for calculation and programming; writing functions and modules for code re-use; creating new classes and programming with objects; Python including command line and notebooks. The module will conclude with an introduction to numerical Python: using numpy and pandas for data analysis; plotting basic graphics.

Introduction to applied epidemiology

This is an introductory module on epidemiology with a strong focus on applied and quantitative topics.The module introduces key epidemiological concepts, including types of health outcomes, definitions of exposure and risk, and metrics used to quantify disease in a population. The students will be given an overview of the most commonly used epidemiological study designs: case-control studies, randomized control trials and cohort studies. Limitations and issues arising from recruitment and sampling biases will be discussed for each design. An important topic of the module will be how to draw inferences from epidemiological studies, defining association, causality and how these differ. In the lab sessions of the module, the students will analyse epidemiological data using R statistical programming language.

Fundamentals of Health Data Science

This module provides students with a formal understanding of research methods, and develops their ability to critically reflect on research approaches and practices in the field. On completion of this module students will be able: to understand what the data science role entails, and how that individual performs their job within an organisation on a day-to-day basis; to understand how research is performed in terms of formulating a hypothesis and the implications of research findings, and be aware of different research strategies and when these should be applied; to gain an understanding of data processing, preparation and integration, and how this enables research to be performed; to critique research proposals in terms of their ethical implications; to learn how data science problems are tackled in an industrial settings, and how such findings are communicated to people within the organisation


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